For problems such as small size, low resolution and not obvious features of mobile phones in the monitoring images, it has brought difficulties to the study of the detection algorithm. This article proposes an improved YOLOv5 network model method to identify the use of mobile phones. The improved detection algorithm introduces the lightweight network GhostNet as the main extraction network, and the GhostConv module, the C3Ghost module instead of the Conv basic convolution module and the C3 module in the main network to reduce the network parameters and complexity; at the same time, the attention mechanism CBAM is introduced into the main network, reducing the effects of redundant characteristics after fusion, and extraction of more critical feature information in the target area; using four scale feature detection, the corresponding increase of the detection layer on the basis of the algorithm, to improve the detection accuracy of smaller targets. The experimental results show that, the accuracy of the improved YOLOv5 algorithm is 95.7%, and the mAP is 97.1%, the accuracy and mAP of the training increased by 2.5% and 1.8%, the calculation and parameters were reduced by 14.3% and 24.5%. The improved YOLOv5 algorithm not only has the advantages of lightweight, but also ensures the mAP and accuracy. This method provides theoretical basis and technical reference for the use of mobile phones in the intelligent monitoring technology industry.